Prediction error scenario mining for machine learning models

ABSTRACT

Provided are methods for prediction error scenario mining for machine learning methods, which can include determining a prediction error indicative of a difference between a planned decision of an autonomous vehicle and an ideal decision of the autonomous vehicle. The prediction error is associated with an error-prone scenario for which a machine learning model of an autonomous vehicle is to make planned movements. The method includes searching a scenario database for the error-prone scenario based on the prediction error. The scenario database includes a plurality of datasets representative of data received from an autonomous vehicle sensor system in which the plurality of datasets is marked with at least one attribute of the set of attributes. The method further includes obtaining the error-prone scenario from the scenario database for inputting into the machine learning model for training the machine learning model. Systems and computer program products are also provided.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.17/673,633 filed on Feb. 16, 2022, entitled “PREDICTION ERROR SCENARIOMINING FOR MACHINE LEARNING MODELS”. The disclosures of which areincorporated herein by reference in its entirety.

BACKGROUND

An autonomous vehicle is a vehicle that is capable of sensing itsenvironment and navigating without human input. Autonomous vehicles relyon multiple types of sensors to perceive the surrounding environment.The sensors provide the autonomous vehicle with data representative ofthe surrounding environment. The autonomous vehicle performs variousprocessing techniques on the data to make safe and correct movementdecisions. These decisions safely navigate the autonomous vehicle tochoose a path to avoid obstacles and react to a variety of differentdriving scenarios, such as the abrupt movements of proximate vehicles.

Testing all conditions and scenarios that an autonomous vehicle mustnavigate is generally dangerous and unfeasible in real-world drivingenvironments. Moreover, conventional simulators typically do not test avariety of different driving scenarios and fail to identify or testmovement decisions of the autonomous vehicle in error-prone scenarios.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is an example environment in which a vehicle including one ormore components of an autonomous system can be implemented;

FIG. 2 is a diagram of one or more systems of a vehicle including anautonomous system;

FIG. 3 is a diagram of components of one or more devices and/or one ormore systems of FIGS. 1 and 2 ;

FIG. 4 is a diagram of certain components of an autonomous system;

FIG. 5 is a diagram of an implementation of a prediction error scenariomining framework for querying a scenario database to obtain anerror-prone scenario;

FIG. 6 is a diagram of an implementation of a prediction error datastore;

FIG. 7 is a diagram of an implementation of a process for determiningwhether to retrain a machine learning model based on running the errorprone scenario with an auto-labeled perception;

FIG. 8 is a diagram of an implementation of a process for determining aset of attributes to present to a scenario database;

FIG. 9 is a diagram of an implementation of an attribute data store;

FIG. 10A is a diagram of an implementation of a user interface forgenerating a search string for querying the scenario database;

FIG. 10B is a diagram of another implementation of the user interfacefor generating the search string for querying the scenario database;

FIG. 11 is a diagram of an implementation of a scenario database;

FIG. 12 is a diagram of an example of a user interface for the matchingsimulations data store; and

FIG. 13 is a flowchart of a process for a prediction error scenariomining for machine learning models.

DETAILED DESCRIPTION

In the following description numerous specific details are set forth inorder to provide a thorough understanding of the present disclosure forthe purposes of explanation. It will be apparent, however, that theembodiments described by the present disclosure can be practiced withoutthese specific details. In some instances, well-known structures anddevices are illustrated in block diagram form in order to avoidunnecessarily obscuring aspects of the present disclosure.

Specific arrangements or orderings of schematic elements, such as thoserepresenting systems, devices, modules, instruction blocks, dataelements, and/or the like are illustrated in the drawings for ease ofdescription. However, it will be understood by those skilled in the artthat the specific ordering or arrangement of the schematic elements inthe drawings is not meant to imply that a particular order or sequenceof processing, or separation of processes, is required unless explicitlydescribed as such. Further, the inclusion of a schematic element in adrawing is not meant to imply that such element is required in allembodiments or that the features represented by such element may not beincluded in or combined with other elements in some embodiments unlessexplicitly described as such.

Further, where connecting elements such as solid or dashed lines orarrows are used in the drawings to illustrate a connection,relationship, or association between or among two or more otherschematic elements, the absence of any such connecting elements is notmeant to imply that no connection, relationship, or association canexist. In other words, some connections, relationships, or associationsbetween elements are not illustrated in the drawings so as not toobscure the disclosure. In addition, for ease of illustration, a singleconnecting element can be used to represent multiple connections,relationships, or associations between elements. For example, where aconnecting element represents communication of signals, data, orinstructions (e.g., “software instructions”), it should be understood bythose skilled in the art that such element can represent one or multiplesignal paths (e.g., a bus), as may be needed, to affect thecommunication.

Although the terms first, second, third, and/or the like are used todescribe various elements, these elements should not be limited by theseterms. The terms first, second, third, and/or the like are used only todistinguish one element from another. For example, a first contact couldbe termed a second contact and, similarly, a second contact could betermed a first contact without departing from the scope of the describedembodiments. The first contact and the second contact are both contacts,but they are not the same contact.

The terminology used in the description of the various describedembodiments herein is included for the purpose of describing particularembodiments only and is not intended to be limiting. As used in thedescription of the various described embodiments and the appendedclaims, the singular forms “a,” “an” and “the” are intended to includethe plural forms as well and can be used interchangeably with “one ormore” or “at least one,” unless the context clearly indicates otherwise.It will also be understood that the term “and/or” as used herein refersto and encompasses any and all possible combinations of one or more ofthe associated listed items. It will be further understood that theterms “includes,” “including,” “comprises,” and/or “comprising,” whenused in this description specify the presence of stated features,integers, steps, operations, elements, and/or components, but do notpreclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof.

As used herein, the terms “communication” and “communicate” refer to atleast one of the reception, receipt, transmission, transfer, provision,and/or the like of information (or information represented by, forexample, data, signals, messages, instructions, commands, and/or thelike). For one unit (e.g., a device, a system, a component of a deviceor system, combinations thereof, and/or the like) to be in communicationwith another unit means that the one unit is able to directly orindirectly receive information from and/or send (e.g., transmit)information to the other unit. This may refer to a direct or indirectconnection that is wired and/or wireless in nature. Additionally, twounits may be in communication with each other even though theinformation transmitted may be modified, processed, relayed, and/orrouted between the first and second unit. For example, a first unit maybe in communication with a second unit even though the first unitpassively receives information and does not actively transmitinformation to the second unit. As another example, a first unit may bein communication with a second unit if at least one intermediary unit(e.g., a third unit located between the first unit and the second unit)processes information received from the first unit and transmits theprocessed information to the second unit. In some embodiments, a messagemay refer to a network packet (e.g., a data packet and/or the like) thatincludes data.

As used herein, the term “if” is, optionally, construed to mean “when”,“upon”, “in response to determining,” “in response to detecting,” and/orthe like, depending on the context. Similarly, the phrase “if it isdetermined” or “if [a stated condition or event] is detected” is,optionally, construed to mean “upon determining,” “in response todetermining,” “upon detecting [the stated condition or event],” “inresponse to detecting [the stated condition or event],” and/or the like,depending on the context. Also, as used herein, the terms “has”, “have”,“having”, or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based at least partially on”unless explicitly stated otherwise.

Reference will now be made in detail to embodiments, examples of whichare illustrated in the accompanying drawings. In the following detaileddescription, numerous specific details are set forth in order to providea thorough understanding of the various described embodiments. However,it will be apparent to one of ordinary skill in the art that the variousdescribed embodiments can be practiced without these specific details.In other instances, well-known methods, procedures, components,circuits, and networks have not been described in detail so as not tounnecessarily obscure aspects of the embodiments.

General Overview

In some aspects and/or embodiments, systems, methods, and computerprogram products described herein include and/or implement predictionerror scenario mining for machine learning models. A scenario miningsystem can identify uncommon scenarios potentially encountered by avehicle (such as an autonomous vehicle). Scenario mining is a techniquein which driving scenarios are identified from a database of drivingscenarios to further train a machine learning model of a vehicle. Forexample, the driving scenarios that are error-prone for the machinelearning model may be mined to further train the machine learning modelto safely respond to these problematic driving scenarios. Scenariomining is carried out by searching a scenario database based on aprediction error to identify the driving scenario of interest. Further,scenario mining is performed for the machine learning model in order toassess how the vehicle's systems would respond to rare and edge-casescenarios. As an example technique, a scenario mining system determinesa prediction error associated with an error-prone scenario for which amachine learning model of a vehicle is to make planned movements. Theerror-prone scenario includes a prediction error indicative of adifference between a planned decision of the vehicle and an idealdecision of the vehicle. For example, an error-prone scenario for themachine learning model has a prediction error in predicting a plannedsafe trajectory instead of a head-on collision in response to apredicted movement of an agent vehicle while the autonomous vehiclemaking an unprotected turn.

The prediction error is associated with an error-prone scenario forwhich the machine learning model of the autonomous vehicle is to makeplanned decisions. The error-prone scenario is identified based on theprediction error in the scenario database. The scenario databaseincludes a plurality of datasets representative of data received from avehicle sensor's system. The error-prone scenario from the scenariodatabase is then obtained for inputting into the machine learning modelfor training the machine learning model. The error-prone scenarioincludes a dataset with the prediction error found in the scenariodatabase. This technique identifies error-prone scenarios to determinehow the vehicle's systems would handle such scenarios in the real world.

By virtue of the implementation of systems, methods, and computerprogram products described herein, techniques for prediction errorscenario mining for machine learning models. Unlike other methods fortraining machine learning models, the scenario mining frameworkdescribed herein includes techniques for mining scenarios in a scenariodatabase based on prediction errors. The scenario database includes aplurality of datasets to be mined, the plurality of datasets includingerror metadata indicative of a prediction error of the machine learningmodel. Without the ability to mine the scenario database, brute forcetraining and experimentation would be the costly and inefficientalternative for the creation of safe and comprehensive autonomousvehicle machine learning models. Brute force testing requires everypossible scenario to be inputted to the machine learning model. Not onlyis this inefficient and costly, but brute force testing fails to adaptand predict new error-prone scenarios that drivers and autonomousvehicles encounter alike. Further, brute force search testing isinsufficient as the number of potential edge-case and error-pronescenarios constantly increase and change over time. As a technicalimprovement, the scenario database described herein mines for complexscenarios using SQL queries using prediction errors of these complexscenarios.

Further, the prediction error searching system solves technical problemsassociated with training a machine learning model configured to makeplanned movements for an autonomous vehicle. Technical problems includeobtaining evaluation metrics for error-prone scenarios to ensureautonomous vehicles are safe for rare and edge-case scenarios. Forexample, a machine learning model trained in a variety of city scenariosmay behave poorly or even dangerously when the model predicts a safetrajectory instead of a head-on collision in response to predicting amovement of a land mammal on a country road. Without identifyingerror-prone scenarios and their associated metrics, it may be unclearthe extent of the effect that any one of these uncommon scenarios wouldhave on the autonomous vehicle's ability to continue navigation.

Other technical problems include a lack of a data model showing that themachine learning model is properly trained for uncommon and error-pronescenarios beyond the natural distribution of driving scenarios. Forexample, manual scenario mining may be overly concerned by the effectsof large trucks making unprotected turns on the planned movements of themachine learning model. But the underdeveloped area of the machinelearning model may be the planned movements of the autonomous vehiclesin response to the predicted movements of bicyclists at four-way stops.Where a human operator would overlook this edge case scenario, thescenario mining framework based on prediction errors described hereintakes a data-driven approach to ensure that the machine learning modelis trained for the most uncommon and error-prone scenarios. Without thisdata-driven approach, it is unclear whether the machine learning modelcan make safe planned movements for the autonomous vehicle. As such,there is a need for a system to search and obtain an error-pronescenario having a prediction error at a scenario database.

The architecture of the prediction error scenario mining framework andcombination of steps to implement the scenario mining improves onexisting frameworks and methods. For example, other frameworks requirerunning a new scenario miner on all of the logged datasets for each newscenario of interest. Such simulations are costly, fail to considerscenarios in which prediction errors are relevant, and cannotdynamically adjust searches for optimal results. In contrast, thearchitecture of the prediction error scenario mining framework providesthe most comprehensive results by creating a scenario database in whichall existing prediction errors across all relevant datasets may bedynamically searched for in a single process.

Further, the architecture of the prediction error scenario miningframework marks and accesses multiple sources of logged datasets toidentify the most error-prone scenarios that the autonomous vehicle cannavigate. This marking and accessing allow new prediction errors andscenarios to be dynamically integrated by adding error metadata to thedata received from the autonomous vehicle sensor system. Additionally,the machine learning model continually improves on its own by iteratingon data in addition to algorithms. For example, error-prone scenariosare flagged up and stored in the scenario database for later evaluation,which provides data-driven insight into the focus of algorithmicdevelopment for the machine learning model. The usefulness of thescenario mining described herein results in a higher likelihood that theautonomous vehicle stack will perform well when planning movements inreal scenarios.

Referring now to FIG. 1 , illustrated is example environment 100 inwhich vehicles that include autonomous systems, as well as vehicles thatdo not, are operated. As illustrated, environment 100 includes vehicles102 a-102 n, objects 104 a-104 n, routes 106 a-106 n, area 108,vehicle-to-infrastructure (V2I) device 110, network 112, remoteautonomous vehicle (AV) system 114, fleet management system 116, and V2Isystem 118. Vehicles 102 a-102 n, vehicle-to-infrastructure (V2I) device110, network 112, autonomous vehicle (AV) system 114, fleet managementsystem 116, and V2I system 118 interconnect (e.g., establish aconnection to communicate and/or the like) via wired connections,wireless connections, or a combination of wired or wireless connections.In some embodiments, objects 104 a-104 n interconnect with at least oneof vehicles 102 a-102 n, vehicle-to-infrastructure (V2I) device 110,network 112, autonomous vehicle (AV) system 114, fleet management system116, and V2I system 118 via wired connections, wireless connections, ora combination of wired or wireless connections.

Vehicles 102 a-102 n (referred to individually as vehicle 102 andcollectively as vehicles 102) include at least one device configured totransport goods and/or people. In some embodiments, vehicles 102 areconfigured to be in communication with V2I device 110, remote AV system114, fleet management system 116, and/or V2I system 118 via network 112.In some embodiments, vehicles 102 include cars, buses, trucks, trains,and/or the like. In some embodiments, vehicles 102 are the same as, orsimilar to, vehicles 200, described herein (see FIG. 2 ). In someembodiments, a vehicle 200 of a set of vehicles 200 is associated withan autonomous fleet manager. In some embodiments, vehicles 102 travelalong respective routes 106 a-106 n (referred to individually as route106 and collectively as routes 106), as described herein. In someembodiments, one or more vehicles 102 include an autonomous system(e.g., an autonomous system that is the same as or similar to autonomoussystem 202).

Objects 104 a-104 n (referred to individually as object 104 andcollectively as objects 104) include, for example, at least one vehicle,at least one pedestrian, at least one cyclist, at least one structure(e.g., a building, a sign, a fire hydrant, etc.), and/or the like. Eachobject 104 is stationary (e.g., located at a fixed location for a periodof time) or mobile (e.g., having a velocity and associated with at leastone trajectory). In some embodiments, objects 104 are associated withcorresponding locations in area 108.

Routes 106 a-106 n (referred to individually as route 106 andcollectively as routes 106) are each associated with (e.g., prescribe) asequence of actions (also known as a trajectory) connecting states alongwhich an AV can navigate. Each route 106 starts at an initial state(e.g., a state that corresponds to a first spatiotemporal location,velocity, and/or the like) and a final goal state (e.g., a state thatcorresponds to a second spatiotemporal location that is different fromthe first spatiotemporal location) or goal region (e.g. a subspace ofacceptable states (e.g., terminal states)). In some embodiments, thefirst state includes a location at which an individual or individualsare to be picked-up by the AV and the second state or region includes alocation or locations at which the individual or individuals picked-upby the AV are to be dropped-off. In some embodiments, routes 106 includea plurality of acceptable state sequences (e.g., a plurality ofspatiotemporal location sequences), the plurality of state sequencesassociated with (e.g., defining) a plurality of trajectories. In anexample, routes 106 include only high level actions or imprecise statelocations, such as a series of connected roads dictating turningdirections at roadway intersections. Additionally, or alternatively,routes 106 may include more precise actions or states such as, forexample, specific target lanes or precise locations within the laneareas and targeted speed at those positions. In an example, routes 106include a plurality of precise state sequences along the at least onehigh level action sequence with a limited lookahead horizon to reachintermediate goals, where the combination of successive iterations oflimited horizon state sequences cumulatively correspond to a pluralityof trajectories that collectively form the high level route to terminateat the final goal state or region.

Area 108 includes a physical area (e.g., a geographic region) withinwhich vehicles 102 can navigate. In an example, area 108 includes atleast one state (e.g., a country, a province, an individual state of aplurality of states included in a country, etc.), at least one portionof a state, at least one city, at least one portion of a city, etc. Insome embodiments, area 108 includes at least one named thoroughfare(referred to herein as a “road”) such as a highway, an interstatehighway, a parkway, a city street, etc. Additionally, or alternatively,in some examples area 108 includes at least one unnamed road such as adriveway, a section of a parking lot, a section of a vacant and/orundeveloped lot, a dirt path, etc. In some embodiments, a road includesat least one lane (e.g., a portion of the road that can be traversed byvehicles 102). In an example, a road includes at least one laneassociated with (e.g., identified based on) at least one lane marking.

Vehicle-to-Infrastructure (V2I) device 110 (sometimes referred to as aVehicle-to-Infrastructure (V2X) device) includes at least one deviceconfigured to be in communication with vehicles 102 and/or V2Iinfrastructure system 118. In some embodiments, V2I device 110 isconfigured to be in communication with vehicles 102, remote AV system114, fleet management system 116, and/or V2I system 118 via network 112.In some embodiments, V2I device 110 includes a radio frequencyidentification (RFID) device, signage, cameras (e.g., two-dimensional(2D) and/or three-dimensional (3D) cameras), lane markers, streetlights,parking meters, etc. In some embodiments, V2I device 110 is configuredto communicate directly with vehicles 102. Additionally, oralternatively, in some embodiments V2I device 110 is configured tocommunicate with vehicles 102, remote AV system 114, and/or fleetmanagement system 116 via V2I system 118. In some embodiments, V2Idevice 110 is configured to communicate with V2I system 118 via network112.

Network 112 includes one or more wired and/or wireless networks. In anexample, network 112 includes a cellular network (e.g., a long termevolution (LTE) network, a third generation (3G) network, a fourthgeneration (4G) network, a fifth generation (5G) network, a codedivision multiple access (CDMA) network, etc.), a public land mobilenetwork (PLMN), a local area network (LAN), a wide area network (WAN), ametropolitan area network (MAN), a telephone network (e.g., the publicswitched telephone network (PSTN), a private network, an ad hoc network,an intranet, the Internet, a fiber optic-based network, a cloudcomputing network, etc., a combination of some or all of these networks,and/or the like.

Remote AV system 114 includes at least one device configured to be incommunication with vehicles 102, V2I device 110, network 112, remote AVsystem 114, fleet management system 116, and/or V2I system 118 vianetwork 112. In an example, remote AV system 114 includes a server, agroup of servers, and/or other like devices. In some embodiments, remoteAV system 114 is co-located with the fleet management system 116. Insome embodiments, remote AV system 114 is involved in the installationof some or all of the components of a vehicle, including an autonomoussystem, an autonomous vehicle compute, software implemented by anautonomous vehicle compute, and/or the like. In some embodiments, remoteAV system 114 maintains (e.g., updates and/or replaces) such componentsand/or software during the lifetime of the vehicle.

Fleet management system 116 includes at least one device configured tobe in communication with vehicles 102, V2I device 110, remote AV system114, and/or V2I infrastructure system 118. In an example, fleetmanagement system 116 includes a server, a group of servers, and/orother like devices. In some embodiments, fleet management system 116 isassociated with a ridesharing company (e.g., an organization thatcontrols operation of multiple vehicles (e.g., vehicles that includeautonomous systems and/or vehicles that do not include autonomoussystems) and/or the like).

In some embodiments, V2I system 118 includes at least one deviceconfigured to be in communication with vehicles 102, V2I device 110,remote AV system 114, and/or fleet management system 116 via network112. In some examples, V2I system 118 is configured to be incommunication with V2I device 110 via a connection different fromnetwork 112. In some embodiments, V2I system 118 includes a server, agroup of servers, and/or other like devices. In some embodiments, V2Isystem 118 is associated with a municipality or a private institution(e.g., a private institution that maintains V2I device 110 and/or thelike).

The number and arrangement of elements illustrated in FIG. 1 areprovided as an example. There can be additional elements, fewerelements, different elements, and/or differently arranged elements, thanthose illustrated in FIG. 1 . Additionally, or alternatively, at leastone element of environment 100 can perform one or more functionsdescribed as being performed by at least one different element of FIG. 1. Additionally, or alternatively, at least one set of elements ofenvironment 100 can perform one or more functions described as beingperformed by at least one different set of elements of environment 100.

Referring now to FIG. 2 , vehicle 200 includes autonomous system 202,powertrain control system 204, steering control system 206, and brakesystem 208. In some embodiments, vehicle 200 is the same as or similarto vehicle 102 (see FIG. 1 ). In some embodiments, vehicle 102 haveautonomous capability (e.g., implement at least one function, feature,device, and/or the like that enable vehicle 200 to be partially or fullyoperated without human intervention including, without limitation, fullyautonomous vehicles (e.g., vehicles that forego reliance on humanintervention), highly autonomous vehicles (e.g., vehicles that foregoreliance on human intervention in certain situations), and/or the like).For a detailed description of fully autonomous vehicles and highlyautonomous vehicles, reference may be made to SAE International'sstandard J3016: Taxonomy and Definitions for Terms Related to On-RoadMotor Vehicle Automated Driving Systems, which is incorporated byreference in its entirety. In some embodiments, vehicle 200 isassociated with an autonomous fleet manager and/or a ridesharingcompany.

Autonomous system 202 includes a sensor suite that includes one or moredevices such as cameras 202 a, LiDAR sensors 202 b, radar sensors 202 c,and microphones 202 d. In some embodiments, autonomous system 202 caninclude more or fewer devices and/or different devices (e.g., ultrasonicsensors, inertial sensors, GPS receivers (discussed below), odometrysensors that generate data associated with an indication of a distancethat vehicle 200 has traveled, and/or the like). In some embodiments,autonomous system 202 uses the one or more devices included inautonomous system 202 to generate data associated with environment 100,described herein. The data generated by the one or more devices ofautonomous system 202 can be used by one or more systems describedherein to observe the environment (e.g., environment 100) in whichvehicle 200 is located. In some embodiments, autonomous system 202includes communication device 202 e, autonomous vehicle compute 202 f,and drive-by-wire (DBW) system 202 h.

Cameras 202 a include at least one device configured to be incommunication with communication device 202 e, autonomous vehiclecompute 202 f, and/or safety controller 202 g via a bus (e.g., a busthat is the same as or similar to bus 302 of FIG. 3 ). Cameras 202 ainclude at least one camera (e.g., a digital camera using a light sensorsuch as a charge-coupled device (CCD), a thermal camera, an infrared(IR) camera, an event camera, and/or the like) to capture imagesincluding physical objects (e.g., cars, buses, curbs, people, and/or thelike). In some embodiments, camera 202 a generates camera data asoutput. In some examples, camera 202 a generates camera data thatincludes image data associated with an image. In this example, the imagedata may specify at least one parameter (e.g., image characteristicssuch as exposure, brightness, etc., an image timestamp, and/or the like)corresponding to the image. In such an example, the image may be in aformat (e.g., RAW, JPEG, PNG, and/or the like). In some embodiments,camera 202 a includes a plurality of independent cameras configured on(e.g., positioned on) a vehicle to capture images for the purpose ofstereopsis (stereo vision). In some examples, camera 202 a includes aplurality of cameras that generate image data and transmit the imagedata to autonomous vehicle compute 202 f and/or a fleet managementsystem (e.g., a fleet management system that is the same as or similarto fleet management system 116 of FIG. 1 ). In such an example,autonomous vehicle compute 202 f determines depth to one or more objectsin a field of view of at least two cameras of the plurality of camerasbased on the image data from the at least two cameras. In someembodiments, cameras 202 a is configured to capture images of objectswithin a distance from cameras 202 a (e.g., up to 100 meters, up to akilometer, and/or the like). Accordingly, cameras 202 a include featuressuch as sensors and lenses that are optimized for perceiving objectsthat are at one or more distances from cameras 202 a.

In an embodiment, camera 202 a includes at least one camera configuredto capture one or more images associated with one or more trafficlights, street signs and/or other physical objects that provide visualnavigation information. In some embodiments, camera 202 a generatestraffic light data associated with one or more images. In some examples,camera 202 a generates TLD data associated with one or more images thatinclude a format (e.g., RAW, JPEG, PNG, and/or the like). In someembodiments, camera 202 a that generates TLD data differs from othersystems described herein incorporating cameras in that camera 202 a caninclude one or more cameras with a wide field of view (e.g., awide-angle lens, a fish-eye lens, a lens having a viewing angle ofapproximately 120 degrees or more, and/or the like) to generate imagesabout as many physical objects as possible.

Laser Detection and Ranging (LiDAR) sensors 202 b include at least onedevice configured to be in communication with communication device 202e, autonomous vehicle compute 202 f, and/or safety controller 202 g viaa bus (e.g., a bus that is the same as or similar to bus 302 of FIG. 3). LiDAR sensors 202 b include a system configured to transmit lightfrom a light emitter (e.g., a laser transmitter). Light emitted by LiDARsensors 202 b include light (e.g., infrared light and/or the like) thatis outside of the visible spectrum. In some embodiments, duringoperation, light emitted by LiDAR sensors 202 b encounters a physicalobject (e.g., a vehicle) and is reflected back to LiDAR sensors 202 b.In some embodiments, the light emitted by LiDAR sensors 202 b does notpenetrate the physical objects that the light encounters. LiDAR sensors202 b also include at least one light detector which detects the lightthat was emitted from the light emitter after the light encounters aphysical object. In some embodiments, at least one data processingsystem associated with LiDAR sensors 202 b generates an image (e.g., apoint cloud, a combined point cloud, and/or the like) representing theobjects included in a field of view of LiDAR sensors 202 b. In someexamples, the at least one data processing system associated with LiDARsensor 202 b generates an image that represents the boundaries of aphysical object, the surfaces (e.g., the topology of the surfaces) ofthe physical object, and/or the like. In such an example, the image isused to determine the boundaries of physical objects in the field ofview of LiDAR sensors 202 b.

Radio Detection and Ranging (radar) sensors 202 c include at least onedevice configured to be in communication with communication device 202e, autonomous vehicle compute 202 f, and/or safety controller 202 g viaa bus (e.g., a bus that is the same as or similar to bus 302 of FIG. 3). Radar sensors 202 c include a system configured to transmit radiowaves (either pulsed or continuously). The radio waves transmitted byradar sensors 202 c include radio waves that are within a predeterminedspectrum In some embodiments, during operation, radio waves transmittedby radar sensors 202 c encounter a physical object and are reflectedback to radar sensors 202 c. In some embodiments, the radio wavestransmitted by radar sensors 202 c are not reflected by some objects. Insome embodiments, at least one data processing system associated withradar sensors 202 c generates signals representing the objects includedin a field of view of radar sensors 202 c. For example, the at least onedata processing system associated with radar sensor 202 c generates animage that represents the boundaries of a physical object, the surfaces(e.g., the topology of the surfaces) of the physical object, and/or thelike. In some examples, the image is used to determine the boundaries ofphysical objects in the field of view of radar sensors 202 c.

Microphones 202 d includes at least one device configured to be incommunication with communication device 202 e, autonomous vehiclecompute 202 f, and/or safety controller 202 g via a bus (e.g., a busthat is the same as or similar to bus 302 of FIG. 3 ). Microphones 202 dinclude one or more microphones (e.g., array microphones, externalmicrophones, and/or the like) that capture audio signals and generatedata associated with (e.g., representing) the audio signals. In someexamples, microphones 202 d include transducer devices and/or likedevices. In some embodiments, one or more systems described herein canreceive the data generated by microphones 202 d and determine a positionof an object relative to vehicle 200 (e.g., a distance and/or the like)based on the audio signals associated with the data.

Communication device 202 e include at least one device configured to bein communication with cameras 202 a, LiDAR sensors 202 b, radar sensors202 c, microphones 202 d, autonomous vehicle compute 202 f, safetycontroller 202 g, and/or DBW system 202 h. For example, communicationdevice 202 e may include a device that is the same as or similar tocommunication interface 314 of FIG. 3 . In some embodiments,communication device 202 e includes a vehicle-to-vehicle (V2V)communication device (e.g., a device that enables wireless communicationof data between vehicles).

Autonomous vehicle compute 202 f include at least one device configuredto be in communication with cameras 202 a, LiDAR sensors 202 b, radarsensors 202 c, microphones 202 d, communication device 202 e, safetycontroller 202 g, and/or DBW system 202 h. In some examples, autonomousvehicle compute 202 f includes a device such as a client device, amobile device (e.g., a cellular telephone, a tablet, and/or the like) aserver (e.g., a computing device including one or more centralprocessing units, graphical processing units, and/or the like), and/orthe like. In some embodiments, autonomous vehicle compute 202 f is thesame as or similar to autonomous vehicle compute 400, described herein.Additionally, or alternatively, in some embodiments autonomous vehiclecompute 202 f is configured to be in communication with an autonomousvehicle system (e.g., an autonomous vehicle system that is the same asor similar to remote AV system 114 of FIG. 1 ), a fleet managementsystem (e.g., a fleet management system that is the same as or similarto fleet management system 116 of FIG. 1 ), a V2I device (e.g., a V2Idevice that is the same as or similar to V2I device 110 of FIG. 1 ),and/or a V2I system (e.g., a V2I system that is the same as or similarto V2I system 118 of FIG. 1 ).

Safety controller 202 g includes at least one device configured to be incommunication with cameras 202 a, LiDAR sensors 202 b, radar sensors 202c, microphones 202 d, communication device 202 e, autonomous vehiclecomputer 202 f, and/or DBW system 202 h. In some examples, safetycontroller 202 g includes one or more controllers (electricalcontrollers, electromechanical controllers, and/or the like) that areconfigured to generate and/or transmit control signals to operate one ormore devices of vehicle 200 (e.g., powertrain control system 204,steering control system 206, brake system 208, and/or the like). In someembodiments, safety controller 202 g is configured to generate controlsignals that take precedence over (e.g., overrides) control signalsgenerated and/or transmitted by autonomous vehicle compute 202 f.

DBW system 202 h includes at least one device configured to be incommunication with communication device 202 e and/or autonomous vehiclecompute 202 f. In some examples, DBW system 202 h includes one or morecontrollers (e.g., electrical controllers, electromechanicalcontrollers, and/or the like) that are configured to generate and/ortransmit control signals to operate one or more devices of vehicle 200(e.g., powertrain control system 204, steering control system 206, brakesystem 208, and/or the like). Additionally, or alternatively, the one ormore controllers of DBW system 202 h are configured to generate and/ortransmit control signals to operate at least one different device (e.g.,a turn signal, headlights, door locks, windshield wipers, and/or thelike) of vehicle 200.

Powertrain control system 204 includes at least one device configured tobe in communication with DBW system 202 h. In some examples, powertraincontrol system 204 includes at least one controller, actuator, and/orthe like. In some embodiments, powertrain control system 204 receivescontrol signals from DBW system 202 h and powertrain control system 204causes vehicle 200 to start moving forward, stop moving forward, startmoving backward, stop moving backward, accelerate in a direction,decelerate in a direction, perform a left turn, perform a right turn,and/or the like. In an example, powertrain control system 204 causes theenergy (e.g., fuel, electricity, and/or the like) provided to a motor ofthe vehicle to increase, remain the same, or decrease, thereby causingat least one wheel of vehicle 200 to rotate or not rotate.

Steering control system 206 includes at least one device configured torotate one or more wheels of vehicle 200. In some examples, steeringcontrol system 206 includes at least one controller, actuator, and/orthe like. In some embodiments, steering control system 206 causes thefront two wheels and/or the rear two wheels of vehicle 200 to rotate tothe left or right to cause vehicle 200 to turn to the left or right.

Brake system 208 includes at least one device configured to actuate oneor more brakes to cause vehicle 200 to reduce speed and/or remainstationary. In some examples, brake system 208 includes at least onecontroller and/or actuator that is configured to cause one or morecalipers associated with one or more wheels of vehicle 200 to close on acorresponding rotor of vehicle 200. Additionally, or alternatively, insome examples brake system 208 includes an automatic emergency braking(AEB) system, a regenerative braking system, and/or the like.

In some embodiments, vehicle 200 includes at least one platform sensor(not explicitly illustrated) that measures or infers properties of astate or a condition of vehicle 200. In some examples, vehicle 200includes platform sensors such as a global positioning system (GPS)receiver, an inertial measurement unit (IMU), a wheel speed sensor, awheel brake pressure sensor, a wheel torque sensor, an engine torquesensor, a steering angle sensor, and/or the like.

Referring now to FIG. 3 , illustrated is a schematic diagram of a device300. As illustrated, device 300 includes processor 304, memory 306,storage component 308, input interface 310, output interface 312,communication interface 314, and bus 302. As shown in FIG. 3 , device300 includes bus 302, processor 304, memory 306, storage component 308,input interface 310, output interface 312, and communication interface314.

Bus 302 includes a component that permits communication among thecomponents of device 300. In some embodiments, processor 304 isimplemented in hardware, software, or a combination of hardware andsoftware. In some examples, processor 304 includes a processor (e.g., acentral processing unit (CPU), a graphics processing unit (GPU), anaccelerated processing unit (APU), and/or the like), a microphone, adigital signal processor (DSP), and/or any processing component (e.g., afield-programmable gate array (FPGA), an application specific integratedcircuit (ASIC), and/or the like) that can be programmed to perform atleast one function. Memory 306 includes random access memory (RAM),read-only memory (ROM), and/or another type of dynamic and/or staticstorage device (e.g., flash memory, magnetic memory, optical memory,and/or the like) that stores data and/or instructions for use byprocessor 304.

Storage component 308 stores data and/or software related to theoperation and use of device 300. In some examples, storage component 308includes a hard disk (e.g., a magnetic disk, an optical disk, amagneto-optic disk, a solid state disk, and/or the like), a compact disc(CD), a digital versatile disc (DVD), a floppy disk, a cartridge, amagnetic tape, a CD-ROM, RAM, PROM, EPROM, FLASH-EPROM, NV-RAM, and/oranother type of computer readable medium, along with a correspondingdrive.

Input interface 310 includes a component that permits device 300 toreceive information, such as via user input (e.g., a touchscreendisplay, a keyboard, a keypad, a mouse, a button, a switch, amicrophone, a camera, and/or the like). Additionally or alternatively,in some embodiments input interface 310 includes a sensor that sensesinformation (e.g., a global positioning system (GPS) receiver, anaccelerometer, a gyroscope, an actuator, and/or the like). Outputinterface 312 includes a component that provides output information fromdevice 300 (e.g., a display, a speaker, one or more light-emittingdiodes (LEDs), and/or the like).

In some embodiments, communication interface 314 includes atransceiver-like component (e.g., a transceiver, a separate receiver andtransmitter, and/or the like) that permits device 300 to communicatewith other devices via a wired connection, a wireless connection, or acombination of wired and wireless connections. In some examples,communication interface 314 permits device 300 to receive informationfrom another device and/or provide information to another device. Insome examples, communication interface 314 includes an Ethernetinterface, an optical interface, a coaxial interface, an infraredinterface, a radio frequency (RF) interface, a universal serial bus(USB) interface, a WiFi® interface, a cellular network interface, and/orthe like.

In some embodiments, device 300 performs one or more processes describedherein. Device 300 performs these processes based on processor 304executing software instructions stored by a computer-readable medium,such as memory 305 and/or storage component 308. A computer-readablemedium (e.g., a non-transitory computer readable medium) is definedherein as a non-transitory memory device. A non-transitory memory deviceincludes memory space located inside a single physical storage device ormemory space spread across multiple physical storage devices.

In some embodiments, software instructions are read into memory 306and/or storage component 308 from another computer-readable medium orfrom another device via communication interface 314. When executed,software instructions stored in memory 306 and/or storage component 308cause processor 304 to perform one or more processes described herein.Additionally or alternatively, hardwired circuitry is used in place ofor in combination with software instructions to perform one or moreprocesses described herein. Thus, embodiments described herein are notlimited to any specific combination of hardware circuitry and softwareunless explicitly stated otherwise.

Memory 306 and/or storage component 308 includes data storage or atleast one data structure (e.g., a database and/or the like). Device 300is capable of receiving information from, storing information in,communicating information to, or searching information stored in thedata storage or the at least one data structure in memory 306 or storagecomponent 308. In some examples, the information includes network data,input data, output data, or any combination thereof.

In some embodiments, device 300 is configured to execute softwareinstructions that are either stored in memory 306 and/or in the memoryof another device (e.g., another device that is the same as or similarto device 300). As used herein, the term “module” refers to at least oneinstruction stored in memory 306 and/or in the memory of another devicethat, when executed by processor 304 and/or by a processor of anotherdevice (e.g., another device that is the same as or similar to device300) cause device 300 (e.g., at least one component of device 300) toperform one or more processes described herein. In some embodiments, amodule is implemented in software, firmware, hardware, and/or the like.

The number and arrangement of components illustrated in FIG. 3 areprovided as an example. In some embodiments, device 300 can includeadditional components, fewer components, different components, ordifferently arranged components than those illustrated in FIG. 3 .Additionally or alternatively, a set of components (e.g., one or morecomponents) of device 300 can perform one or more functions described asbeing performed by another component or another set of components ofdevice 300.

Referring now to FIG. 4 , illustrated is an example block diagram of anautonomous vehicle compute 400 (sometimes referred to as an “AV stack”).As illustrated, autonomous vehicle compute 400 includes perceptionsystem 402 (sometimes referred to as a perception module), planningsystem 404 (sometimes referred to as a planning module), localizationsystem 406 (sometimes referred to as a localization module), controlsystem 408 (sometimes referred to as a control module), and database410. In some embodiments, perception system 402, planning system 404,localization system 406, control system 408, and database 410 areincluded and/or implemented in an autonomous navigation system of avehicle (e.g., autonomous vehicle compute 202 f of vehicle 200).Additionally, or alternatively, in some embodiments perception system402, planning system 404, localization system 406, control system 408,and database 410 are included in one or more standalone systems (e.g.,one or more systems that are the same as or similar to autonomousvehicle compute 400 and/or the like). In some examples, perceptionsystem 402, planning system 404, localization system 406, control system408, and database 410 are included in one or more standalone systemsthat are located in a vehicle and/or at least one remote system asdescribed herein. In some embodiments, any and/or all of the systemsincluded in autonomous vehicle compute 400 are implemented in software(e.g., in software instructions stored in memory), computer hardware(e.g., by microprocessors, microcontrollers, application-specificintegrated circuits [ASICs], Field Programmable Gate Arrays (FPGAs),and/or the like), or combinations of computer software and computerhardware. It will also be understood that, in some embodiments,autonomous vehicle compute 400 is configured to be in communication witha remote system (e.g., an autonomous vehicle system that is the same asor similar to remote AV system 114, a fleet management system 116 thatis the same as or similar to fleet management system 116, a V2I systemthat is the same as or similar to V2I system 118, and/or the like).

In some embodiments, perception system 402 receives data associated withat least one physical object (e.g., data that is used by perceptionsystem 402 to detect the at least one physical object) in an environmentand classifies the at least one physical object. In some examples,perception system 402 receives image data captured by at least onecamera (e.g., cameras 202 a), the image associated with (e.g.,representing) one or more physical objects within a field of view of theat least one camera. In such an example, perception system 402classifies at least one physical object based on one or more groupingsof physical objects (e.g., bicycles, vehicles, traffic signs,pedestrians, and/or the like). In some embodiments, perception system402 transmits data associated with the classification of the physicalobjects to planning system 404 based on perception system 402classifying the physical objects.

In some embodiments, planning system 404 receives data associated with adestination and generates data associated with at least one route (e.g.,routes 106) along which a vehicle (e.g., vehicles 102) can travel alongtoward a destination. In some embodiments, planning system 404periodically or continuously receives data from perception system 402(e.g., data associated with the classification of physical objects,described above) and planning system 404 updates the at least onetrajectory or generates at least one different trajectory based on thedata generated by perception system 402. In some embodiments, planningsystem 404 receives data associated with an updated position of avehicle (e.g., vehicles 102) from localization system 406 and planningsystem 404 updates the at least one trajectory or generates at least onedifferent trajectory based on the data generated by localization system406.

In some embodiments, localization system 406 receives data associatedwith (e.g., representing) a location of a vehicle (e.g., vehicles 102)in an area. In some examples, localization system 406 receives LiDARdata associated with at least one point cloud generated by at least oneLiDAR sensor (e.g., LiDAR sensors 202 b). In certain examples,localization system 406 receives data associated with at least one pointcloud from multiple LiDAR sensors and localization system 406 generatesa combined point cloud based on each of the point clouds. In theseexamples, localization system 406 compares the at least one point cloudor the combined point cloud to two-dimensional (2D) and/or athree-dimensional (3D) map of the area stored in database 410.Localization system 406 then determines the position of the vehicle inthe area based on localization system 406 comparing the at least onepoint cloud or the combined point cloud to the map. In some embodiments,the map includes a combined point cloud of the area generated prior tonavigation of the vehicle. In some embodiments, maps include, withoutlimitation, high-precision maps of the roadway geometric properties,maps describing road network connectivity properties, maps describingroadway physical properties (such as traffic speed, traffic volume, thenumber of vehicular and cyclist traffic lanes, lane width, lane trafficdirections, or lane marker types and locations, or combinationsthereof), and maps describing the spatial locations of road featuressuch as crosswalks, traffic signs or other travel signals of varioustypes. In some embodiments, the map is generated in real-time based onthe data received by the perception system.

In another example, localization system 406 receives Global NavigationSatellite System (GNSS) data generated by a global positioning system(GPS) receiver. In some examples, localization system 406 receives GNSSdata associated with the location of the vehicle in the area andlocalization system 406 determines a latitude and longitude of thevehicle in the area. In such an example, localization system 406determines the position of the vehicle in the area based on the latitudeand longitude of the vehicle. In some embodiments, localization system406 generates data associated with the position of the vehicle. In someexamples, localization system 406 generates data associated with theposition of the vehicle based on localization system 406 determining theposition of the vehicle. In such an example, the data associated withthe position of the vehicle includes data associated with one or moresemantic properties corresponding to the position of the vehicle.

In some embodiments, control system 408 receives data associated with atleast one trajectory from planning system 404 and control system 408controls operation of the vehicle. In some examples, control system 408receives data associated with at least one trajectory from planningsystem 404 and control system 408 controls operation of the vehicle bygenerating and transmitting control signals to cause a powertraincontrol system (e.g., DBW system 202 h, powertrain control system 204,and/or the like), a steering control system (e.g., steering controlsystem 206), and/or a brake system (e.g., brake system 208) to operate.In an example, where a trajectory includes a left turn, control system408 transmits a control signal to cause steering control system 206 toadjust a steering angle of vehicle 200, thereby causing vehicle 200 toturn left. Additionally, or alternatively, control system 408 generatesand transmits control signals to cause other devices (e.g., headlights,turn signal, door locks, windshield wipers, and/or the like) of vehicle200 to change states.

In some embodiments, perception system 402, planning system 404,localization system 406, and/or control system 408 implement at leastone machine learning model (e.g., at least one multilayer perceptron(MLP), at least one convolutional neural network (CNN), at least onerecurrent neural network (RNN), at least one autoencoder, at least onetransformer, and/or the like). In some examples, perception system 402,planning system 404, localization system 406, and/or control system 408implement at least one machine learning model alone or in combinationwith one or more of the above-noted systems. In some examples,perception system 402, planning system 404, localization system 406,and/or control system 408 implement at least one machine learning modelas part of a pipeline (e.g., a pipeline for identifying one or moreobjects located in an environment and/or the like).

Database 410 stores data that is transmitted to, received from, and/orupdated by perception system 402, planning system 404, localizationsystem 406 and/or control system 408. In some examples, database 410includes a storage component (e.g., a storage component that is the sameas or similar to storage component 308 of FIG. 3 ) that stores dataand/or software related to the operation and uses at least one system ofautonomous vehicle compute 400. In some embodiments, database 410 storesdata associated with 2D and/or 3D maps of at least one area. In someexamples, database 410 stores data associated with 2D and/or 3D maps ofa portion of a city, multiple portions of multiple cities, multiplecities, a county, a state, a State (e.g., a country), and/or the like).In such an example, a vehicle (e.g., a vehicle that is the same as orsimilar to vehicles 102 and/or vehicle 200) can drive along one or moredrivable regions (e.g., single-lane roads, multi-lane roads, highways,back roads, off road trails, and/or the like) and cause at least oneLiDAR sensor (e.g., a LiDAR sensor that is the same as or similar toLiDAR sensors 202 b) to generate data associated with an imagerepresenting the objects included in a field of view of the at least oneLiDAR sensor.

In some embodiments, database 410 can be implemented across a pluralityof devices. In some examples, database 410 is included in a vehicle(e.g., a vehicle that is the same as or similar to vehicles 102 and/orvehicle 200), an autonomous vehicle system (e.g., an autonomous vehiclesystem that is the same as or similar to remote AV system 114, a fleetmanagement system (e.g., a fleet management system that is the same asor similar to fleet management system 116 of FIG. 1 , a V2I system(e.g., a V2I system that is the same as or similar to V2I system 118 ofFIG. 1 ) and/or the like.

Referring now to FIG. 5 , illustrated is a diagram of an implementationof a prediction error scenario mining framework for querying a scenariodatabase to obtain an error-prone scenario 552. The query generator 515is configured to generate a search string for the scenario database 530based on a prediction error from the prediction error data store 510.The query generator 515 may be configured to present a search string tothe scenario database 530. The scenario database 530 may include variousvehicle scenarios and receive additional vehicle scenarios from avehicle log data store 535. The scenario database 530 may return a setof matching simulations to the simulations data store 550 that includesan error-prone scenario 552. The error-prone scenario 552 may beindicative of a scenario in which the planned movement decision of themachine learning model 570 is likely erroneous compared to idealmovement decision. The matching scenarios, including the error-pronescenario 552, may be inputted to the machine learning model 570 near anend of the error-prone scenario 552 mining dataflow 500. The machinelearning model 570 may be further trained on the error-prone scenario552 and configured to make updated planned movements for a vehicle. Theplanned movements or decisions of the machine learning model 570 may bebased on predicted movements of objects in the environment, such as anagent vehicle or a bicyclist.

The prediction error data store 510 may include a prediction error foridentifying an error-prone scenario 552 in the scenario database 530.The prediction error data store 510 may be configured to storeprediction error types and modes related to prediction errors of themachine learning model 570 planning movements for a vehicle. Forexample, a prediction error mode may include an error type relatedmiscalculating a predicted movement of an approaching agent vehicleduring a U-turn that leads to the autonomous vehicle being involved inan accident with the agent vehicle. The prediction error data store 510may include error-prone modes and the error-prone types that aredetermined based on known errors that have occurred when predictionerrors have occurred in the past, including the prediction errorsrelated to the predicted movements of other vehicles. The error modesand error types may be identified based on the previous decisions by theplanning module or machine learning model 570 that caused a driver tointervene. For example, the error modes and error types may be definedby the prediction errors that caused a driver to apply the brakes orrotate the steering wheel. In another example, the error modes or errortypes may be determined by the prediction errors when a performanceevaluator 710 recognizes a difference between a planned decision of anautonomous vehicle and an ideal decision of the autonomous vehicle. Theperformance evaluator 710 may determine that a final displacement error,a false positive prediction, or a perception-based error exists based onthe difference between the planned decision and the ideal decision ofthe autonomous vehicle.

Error-prone modes and error-prone types may be combined to createerror-prone scenarios for further training the machine learning model570. For example, the “snow” mode may be an error-prone feature forreduced traction combined with a “driving between two 18 wheeler trucks”type may be an error-prone feature for reduced visibility that arecombined together to create an enhanced error-prone scenario 552. Inanother example, the error-prone scenario 552 may include combining anerror-prone type that the ego vehicle has a broken sensor with theerror-prone mode of the vehicle coasting down a steep hill. The brokensensor combined with the vehicle coasting down a steep hill may cause aprediction error due to the perception error.

The prediction error data store 510 may be configured to pass aprediction error to a query generator 515. In some embodiments, theprediction error data store 510 may be selected to obtain error-pronescenarios. The error-prone scenarios may be desirable to identify tofurther train the machine learning model 570 and improve the robustnessof the machine learning model 570. For example, a desired error-pronescenario may include an ego vehicle that is stopped proximate to a largetruck at an intersection including a crosswalk with a pedestrian as theego vehicle plans to make an unprotected left-hand turn. To captureerror-prone scenarios similar to this desired error-prone scenario, theerror-prone modes or error-prone types may be selected from theprediction error data store 510 and may include error modes or errortypes related to predictions of an agent vehicle that is a large truck,a pedestrian detected in a crosswalk at an intersection, and a planneddecision to make an unprotected left-hand turn based on movementpredictions of other vehicles. In another example, a desired error-pronescenario may include an ego vehicle having an encounter with largeanimals with unpredictable behavior on country roads at night time. Toobtain this desired scenario, the scenario mining controller 590 mayselect error-prone modes or error-prone types related to night,single-lane highways, and predicting the movement of objects having fourlegs and facial features. In some embodiments, the prediction error,error-prone mode, or error-mode type may be determined by the scenariomining controller 590 or may be hand-selected to obtain specificerror-prone vehicle scenarios.

The query generator 515 may generate a query for the scenario database530 to identify the error-prone scenario 552. The query generator 515may be configured to generate a search string based on the predictionerror. In some embodiments, the query generator 515 may be configured togenerate a search string based on error-prone modes and error-pronetypes. The query generator 515 may be configured to receive theprediction error as input and generate a search string configured toquery the scenario database 530 as output. For example, the querygenerator 515 may format search strings (e.g., generate Boolean logic,add qualifiers and variables to a function call) and obtain propersyntax for search strings (e.g., identify variables representative ofthe prediction error) to form a search string that is configured toperform a query at the scenario database 530 based on the predictionerror. In some embodiments, the query generator 515 may generate thesearch string in response to determining the error-prone modes anderror-prone types associated with the error-prone scenario 552. In someembodiments, the query generator 515 may generate an SQL query.

The scenario database 530 may be configured to receive a predictionerror as input and be configured to return an error-prone scenario 552based on the prediction error as output. That is, the scenario database530 may be configured to mine error-prone scenarios based on theprediction error related to movements of objects in the vehicleenvironment. To facilitate the prediction error scenario mining, theplurality of error-prone scenarios may include metadata for matchingprediction errors from the search string to the error-prone scenario552. For example, the scenario database 530 receives the following errormodes and types: predicted movements of an agent vehicle that is a largetruck, a pedestrian detected in a crosswalk at an intersection, and aplanned decision to make an unprotected left-hand turn. The scenariomining controller 590 may search through the scenario database 530 toidentify an error-prone scenario 552 having metadata that includes anego vehicle that is stopped proximate to a large truck at anintersection including a crosswalk with a pedestrian as the ego vehicleplans to make an unprotected left-hand turn. In another example, thescenario mining controller 590 searches through the scenario database530 to identify the error-prone scenario 552 having metadata thatincludes nighttime conditions, single-lane highways, and predicting themovement of objects having facial features and four-legs.

The scenario database 530 may include a plurality of error-pronescenarios in which each error-prone scenario 552 includes vehicle sensordata associated with one or more timeframes. The plurality oferror-prone scenarios may be obtained from a vehicle log data store 535,a simulator, or a combination thereof. For example, an error-pronescenario 552 is based on an actual driving environment and sensor databut may include artificially inserted walls or obstacles. An error-pronescenario 552 may include a plurality of datasets representative of datareceived from an autonomous vehicle sensor system including a LIDARsensor dataset, camera sensor dataset, RADAR sensor dataset, telemetricssensor dataset, and other ego vehicle sensor datasets. In someembodiments, the scenario database 530 may include the presence of asensor in the metadata or a value of the sensor in the metadata. Thescenario mining controller 590 may be configured to search for thepresence and a threshold value of a sensor based on a prediction errorassociated with the sensor. In some embodiments, the scenario database530 may comprise an SQL database and the scenario database 530 may beconfigured to carry out the SQL query.

The vehicle log data store 535 may include data logged by an autonomousvehicle. The logged data may include data that has not yet been taggedwith metadata and uploaded to the scenario database 530. The scenariodatabase 530 may be updated by the vehicle log data store 535 with newscenarios. The scenario mining controller 590 may be configured to addmetadata to error-prone scenarios from the vehicle log data store 535 toupdate the scenario database 530 based on new environments the vehiclehas been in or simulations in which the machine learning model 570 hasbeen trained. The data in the vehicle log data store 535 may includedata from the autonomous vehicle sensor system that is representative ofan environment surrounding the autonomous vehicle.

The matching simulations data store 550 may include a subset of theplurality of error-prone scenarios having metadata matching theprediction error received by the scenario database 530. The subset ofthe plurality of error-prone scenarios may include the error-pronescenario 552. The error-prone scenario 552 may be a scenario in whichthe planning movements of the machine learning model 570 are faultybased on a displacement error threshold related to a predicted movementof another vehicle by the machine learning model 570. For example, thedisplacement error threshold includes a quantitative thresholdrepresentative of a distance between the planned movements and the idealmovements of the machine learning model 570 based on the predictedmovements of the nearby vehicle. The error-prone scenario 552 may be ascenario in which the planning movements of the machine learning model570 are uncertain or faulty based on a timing error threshold related toa movement of another vehicle. For example, the timing error thresholdincludes a quantitative threshold representative of a timing between theplanned movements and the ideal movements of the machine learning model570 based on the predicted movements of the nearby vehicle. Theerror-prone scenario 552 may be a scenario in which the planningmovements of the machine learning model 570 are uncertain or faultybased on a perception threshold. For example, the perception thresholdincludes a quantitative threshold representative of a difference betweenthe perception of the machine learning model 570 in the error-pronescenario 552 compared to the perception of the machine learning model570 in an ideal driving scenario.

The subset of the plurality of error-prone scenarios may be inputted tothe machine learning model 570. In some embodiments, the scenario miningcontroller 590 may obtain the error-prone scenario 552 from the scenariodatabase 530 for inputting into the machine learning model 570 fortraining the machine learning model 570. The machine learning model 570may be configured to make the planned movements for the autonomousvehicle and may be an online perception model or an offline perceptionmodel.

Referring now to FIG. 6 , illustrated is a diagram of an implementationof a prediction error data store. The prediction error data store 510may include a set of attributes for identifying an error-prone scenario552 in the scenario database 530. The prediction error data store 510may be configured to store prediction errors related to miscalculationsmade by the machine learning model 570 related to planned decisions ofthe autonomous vehicle in the environment. The miscalculations of theprediction errors may be determined by comparing the planned movement ofthe machine learning model 570 to the ideal movement of the autonomousvehicle based on the predicted movements of other vehicles by themachine learning model 570. The prediction error data store 510 mayorganize the prediction errors into different types of miscalculations.For example, the types of prediction errors or miscalculations includesperception error predictions 610, false-positive predictions 620, andfinal displacement error predictions 630.

The prediction error data store 510 may include perception errorpredictions 610. Perception error predictions 610 may be selected toobtain error-prone scenarios 552 including a miscalculation due tomisperception by a sensor in the autonomous vehicle sensor system. Forexample, a camera in the autonomous vehicle sensor system isoversaturated by sun exposure that causes a miscalculation in theplanning of an unprotected left-hand turn. To capture error-pronescenarios matching or similar to this desired scenario, the predictionerror selected may include sensor data that is incorrect or sensor datathat is incomplete. In another example, a desired error-prone scenarioincludes a LIDAR sensor broken with no data output or a corrupted dataoutput that causes the planned timing of movements by the machinelearning model 570 to be miscalculated for high-speed agent vehicles.

The prediction error data store 510 may include false-positivepredictions 620. False-positive predictions 620 may be selected toobtain error-prone scenarios 552 including a miscalculation due to anincorrectly analyzing input from the data sensors at the machinelearning model 570 that cause an unnecessary movement. For example, themachine learning model 570 misinterprets data from the sensors to detecta stop sign rather than a flagpole that causes the machine learningmodel 570 to plan an unnecessary stop rather than to proceed through theintersection. To capture error-prone scenarios matching or similar tothis desired scenario, the prediction error selected may includemisinterpreted data that leads to an outcome having unnecessary vehiclemovements. False-positive predictions 620 may be selected to obtainerror-prone scenarios 552 including a miscalculation of a predictedmovement of an object that causes an unnecessary movement planned by themachine learning model 570. For example, a desired error-prone scenarioincludes the miscalculation of a predicted movement of an approachingbicyclist traveling in the opposite direction that causes an unnecessaryturn planned by the machine learning model 570.

The prediction error data store 510 may include final displacement errorpredictions 630. Final displacement error predictions 630 may beselected to obtain error-prone scenarios 552 including a miscalculationin the planned movement by the machine learning model 570 relative tothe ideal movement in the metadata resulting in the autonomous vehiclemoving to a location other than the desired location. For example, themachine learning model 570 miscalculates the distances needed toparallel park, causing the planned movements of the autonomous vehicleto be a distance from the ideal movement of the autonomous vehicleneeded to parallel park. To capture error-prone scenarios matching orsimilar to this desired scenario, the prediction error selected mayinclude bad calculations leading to planned outcome different from anideal outcome. Final displacement error predictions 630 may be selectedto obtain error-prone scenarios 552 including a miscalculation in thepredicted movement of other objects by the machine learning model 570relative to the ideal movement prediction of other objects. For example,a desired error-prone scenario includes the miscalculation of apredicted movement of an approaching bicyclist traveling in the oppositedirection during an unprotected left-hand turn that causes amiscalculation in the movement by the machine learning model 570 andcause a collision with the approaching bicyclist.

In some embodiments, the prediction error may be selected based on theprediction error satisfying a miscalculation threshold. Themiscalculation threshold may be indicative that the machine learningmodel 570 is to be retrained based on the error-prone scenario 552. Forexample, the scenario mining controller 590 selects a final displacementerror prediction in which the planned decision by the machine learningmodel 570 is at least five feet from the ideal trajectory of theautonomous vehicle. In another example, the scenario mining controller590 selects a false positive prediction in which the unnecessarymovement by the machine learning model 570 causes the vehicle todecelerate at a rate of more than five miles per hour per second. Thescenario mining controller 590 may obtain the error-prone scenario 552from the scenario database for inputting into the machine learning model570 in response to the prediction error satisfying the miscalculationthreshold.

Referring now to FIG. 7 , illustrated is a diagram of an implementationof a process for determining whether to retrain the machine learningmodel 570 based on running the error prone scenario with an auto-labeledperception. The scenario database 530 may return a set of matchingsimulations to the simulations data store 550 that includes anerror-prone scenario 552. The error-prone scenario 552 may be indicativeof a scenario in which the planned movement decision of the machinelearning model 570 is likely erroneous compared to an ideal movementdecision. The ideal movement decision may include the correctlypredicted movement of a nearby vehicle or object. The matchingscenarios, including the error-prone scenario 552, may be inputted tothe machine learning model 570. The performance evaluator 710 maydetermine the presence of a miscalculation and a value representative ofthe miscalculation. The performance evaluator 710 may determine that themachine learning model 570 may run the error-prone scenario 552 with animproved auto-labeled perception. The performance evaluator 710 maydetermine the miscalculation in the error-prone scenario 552 ismitigated by running the error-prone scenario 552 with the improvedauto-labeled perception. The scenario mining controller 590 may retrainthe machine learning model 570 with the improved auto-labeled perceptionbased on the mitigated miscalculation in the error-prone scenario 552.

The scenario mining controller 590 may be configured to retrain themachine learning model 570 based on the improved auto-labeled perceptionfor error-prone scenarios. Auto-labeling an error-prone scenario 552where prediction resulted in a miscalculation improves the plannedmovements and prediction metrics of the machine learning model 570. Theretraining machine learning model dataflow 700 may be performed when theprediction error or miscalculation of an error-prone scenario 552satisfies a miscalculation threshold, a difficulty threshold, or asafety threshold. More specifically, the performance evaluator 710 maydetermine whether the prediction error or miscalculation of anerror-prone scenario 552 satisfies a miscalculation threshold, adifficulty threshold, or a safety threshold.

The performance evaluator 710 may be configured to run the machinelearning model 570 on the error-prone scenario 552 with an improvedauto-labeled perception from auto-labeled perception circuit 715. Theimproved auto-labeled perception may include the ideal planned movementfor the error-prone scenario 552. For example, the improved auto-labeledperception includes an auto-labeled trajectory of the autonomous vehiclewhen a sensor is oversaturated by the sun or the auto-labeled mapping ofa detected flag to a flag rather than a stop sign. In another example,the improved auto-labeled trajectory includes an auto-labeled timing ofmaking an unprotected turn for the autonomous vehicle when predictingthe movement of a bicyclist approaching in the opposite direction.

The performance evaluator 710 may be configured to determine whether themachine learning model 570 improves performance, eliminates theprediction error, and mitigates the miscalculation based on running theerror-prone scenario 552. The performance evaluator 710 may determine anupdated prediction error of the error-prone scenario 552 in response torunning the error-prone scenario 552 with the improved auto-labeledperception. For example, the performance evaluator 710 determines anupdated prediction error for the error-prone scenario 552 based onrunning the auto-labeled trajectory when the sensor is oversaturated bythe sun or the updated prediction error for the error-prone scenario 552for running the auto-labeled mapping for the detected flag. In anotherexample, the performance evaluator 710 determines an updated predictionerror for the error-prone scenario 552 based on running the auto-labeledtiming of making an unprotected turn for the autonomous vehicle whenpredicting the movement of the bicyclist approaching in the oppositedirection.

The performance evaluator 710 may determine the updated prediction errorbased on the improved auto-labeled perception is lower than the originalprediction error. For example, the performance evaluator 710 determinesthe updated prediction error based on the improved auto-labeledperception for the error-prone scenario 552 of the sun oversaturating acamera is lower than the prediction error was beforehand. In response todetermining the updated prediction error is lower than the originalprediction error, the scenario mining controller 590 retrains themachine learning model 570 with the improved auto-labeled perception.

Referring now to FIG. 8 , illustrated is a diagram of an implementationof a process for determining a set of attributes to present to ascenario database 530. The query generator 515 may be configured togenerate a search string for the scenario database 530 based on theprediction error and a set of attributes from the attribute data store810. The scenario database 530 may search for a set of matchingsimulations, including the error-prone scenario 552 based on theprediction error and the set of attributes.

The attribute data store 810 may include a set of attributes foridentifying an error-prone scenario 552 in the scenario database 530.The attribute data store 810 may be configured to store attributesrelated to driving in a vehicle environment. The attribute data store810 may include conditions and features that are characteristic ofenvironments that a vehicle may encounter. Conditions and features maybe combined to create vehicle scenarios for further training the machinelearning model 570. For example, the “snow” attribute is a condition anda “driving between two 18 wheeler trucks” feature may be two attributescombined together to create a vehicle scenario. In another example, thevehicle scenario includes combining a condition that the ego vehicle hasa broken sensor with the feature of the vehicle coasting down a steephill.

The attribute data store 810 may be configured to pass a set ofattributes to a query generator 515. In some embodiments, the attributesin the attribute data store 810 may be selected to obtain a specificerror-prone scenario beyond what the prediction error may specify. Thespecific error-prone vehicle scenarios may be desirable to further trainthe machine learning model 570. For example, a desired error-pronescenario includes an ego vehicle that is driving proximate to a largetruck as the ego vehicle and predicting the movements of the large truckapproaching a crosswalk with a pedestrian and a stroller. To capturevehicle scenarios similar to this desired scenario, the attributesselected from the attribute data store 810 may include predictedmovements of an agent vehicle that is a large truck, a pedestriandetected in the crosswalk, a stroller detected in the crosswalk, and theego vehicle is 30 feet away from the crosswalk. In another example, adesired vehicle scenario untested by the machine learning model 570includes an ego vehicle predicting the movements of large animals oncountry roads at night time. To obtain this desired scenario, the queryinclude attributes for a desired scenario that includes a nightattribute, a single-lane highway attribute, and an animal movementprediction attribute.

The set of attributes may be selected by examining the training historyof the machine learning model 570. The training history of the machinelearning model 570 may be stored in the tested scenarios data store 820.The tested scenarios data store 820 may include the scenarios used totest the machine learning model 570. The tested scenarios data store 820may be queried to determine whether a set of attributes is to return aspecific error-prone vehicle scenario.

The set of attributes may be determined by a frequency at which theattribute or the combination of attributes has been tested by themachine learning model 570 or the test history is recorded in the testedscenarios data store 820. The scenario mining controller 590 maydetermine whether a potential attribute should be tested by evaluatingthe frequencies at which each potential attribute or combination ofpotential attributes has been tested by the machine learning model 570or the test history is recorded in the tested scenarios data store 820.The scenario mining controller 590 may compare the test frequencies oftwo or more potential attributes to assist in identifying the specificerror-prone scenarios. The attributes with the lower test frequency maybe selected to identify the untested, rare, and edge-case scenarios.

In comparing the two or more potential attributes, the scenario miningcontroller 590 may determine a first test frequency for which themachine learning model 570 has made the planned movements for theautonomous vehicle based on a first potential attribute. For example,the scenario mining controller 590 determines the number of times (i.e.,first test frequency) the planning system 404 has been presented withpredicting nearby vehicle movements during an unprotected right-handturn (i.e., the first potential attribute). The scenario miningcontroller 590 may determine a second test frequency for which themachine learning model 570 has made the planned movements for theautonomous vehicle based on a second potential attribute. For example,the scenario mining controller 590 determines the number of times (i.e.,second test frequency) the planning system 404 has been presented withpredicting nearby vehicle movements during an unprotected left-hand turn(i.e., the second potential attribute). The scenario mining controller590 may determine the second test frequency is lower than the first testfrequency and select the second potential attribute to add to the set ofattributes based on the second test frequency being lower than the firsttest frequency.

In some other embodiments, the scenario mining controller 590 maydetermine a test frequency threshold representative of the minimumnumber of times an attribute is to be exposed to the machine learningmodel 570 in order to be fully trained. The scenario mining controller590 may search through the potential attributes and determine that asecond attribute fails to satisfy a test frequency threshold and that afirst potential attribute satisfies the test frequency threshold. Inresponse to the second attribute failing to satisfy the test frequencythreshold, the scenario mining controller 590 may select the secondpotential attribute as the attribute to be used to search for a specificerror-prone scenario.

The set of attributes may be determined by a difficulty level associatedwith the potential attribute or the combination of potential attributes.The difficulty rating may be representative of the amount of informationand computations needed to complete the planned movement safely. Thedifficulty rating may be representative of the difficulty for themachine learning model to predict the planned movements of a nearbyobject (e.g., an agent vehicle). The scenario mining controller 590 maydetermine whether a potential attribute should be tested by evaluatingthe difficulty rating of the potential attribute or the combination ofpotential attributes. The scenario mining controller 590 may determinewhether combining a potential attribute with another potential attributeincreases or decreases the difficulty rating associated with thecombination of attributes. The scenario mining controller 590 maycompare the difficulties of two or more potential attributes to assistin identifying the untested, rare, and edge-case scenarios. Theattributes with the higher difficulty rating may be selected to identifythe specific error-prone scenarios. In some embodiments, the difficultyrating may be affected by a safety threshold or rating of a vehiclemaneuver or environment.

In comparing the two or more potential attributes, the scenario miningcontroller 590 may determine a first difficulty rating for the machinelearning model 570 to make the planned movements for the autonomousvehicle based on a first potential attribute. For example, the scenariomining controller 590 determines the difficulty rating based on thefirst potential attribute that is representative of the information andcomputations necessary for the machine learning model 570 to navigatethe vehicle through an unprotected right-hand turn. The scenario miningcontroller 590 may determine a second difficulty rating for the machinelearning model 570 to make the planned movements for the autonomousvehicle based on a second potential attribute. For example, the scenariomining controller 590 determines the difficulty rating based on thesecond potential attribute that is representative of the information andcomputations necessary for the machine learning model 570 to navigatethe vehicle through an unprotected left-hand turn based on the predictedmovements of other vehicles. The scenario mining controller 590 maydetermine the second difficulty rating is greater than the firstdifficulty rating and select the second potential attribute (e.g.,navigating an unprotected left-hand turn based on the predictedmovements of other vehicles) to add to the set of attributes based onthe second difficulty being greater than the first difficulty.

Referring now to FIG. 9 , illustrated is a diagram of an implementationof an attribute data store 810. The attribute data store 810 may includea set of attributes for identifying an error-prone scenario 552 in thescenario database 530. The attribute data store 810 may be configured tostore attributes related to driving in a vehicle environment. Theattribute data store 810 may organize attributes relevant to thevehicle, the environment surrounding the vehicle, or the objects in theenvironment surrounding the vehicle. For example, the types ofattributes include ego attributes 910, agent attributes 920, sceneattributes 930, and custom metric attributes 940.

The attribute data store 810 may include ego attributes 910. Egoattributes 910 may include any features or characteristics of the egovehicle. For example, the ego attribute is a body style (e.g., a truck,a boat) or a means of powering (e.g., a gasoline-powered vehicle, anelectric-powered vehicle). In some embodiments, the ego attribute may berepresentative of a characteristic of the autonomous vehicle. Forexample, the ego vehicle has a wheelbase, a track, a height, a speed, adistance between the ego vehicle and the obstacle, and a turning radius.The ego vehicle may have various sensors including LiDAR sensors, radarsensors, microphones, inertial measurement units (IMUs), a GPS receiver,and real-time kinematics (RTK) receivers. Other ego attributes 910 mayinclude Global Navigation Satellite System (GNSS) data, the latitude andlongitude of the vehicle, or a state where the vehicle is licensed.

The attribute data store 810 may include agent attributes 920. Agentattributes 920 may include any feature or characteristic capable ofmovement on its own, such as a proximate vehicle or an agent vehicle.For example, an agent vehicle is a motorcycle, scooters, a waverunner,an 18-wheel semitruck, a cargo van, a bicycle, and/or the like. Theagent attributes 920 may be representative of a characteristic of theagent vehicle. For example, the agent vehicle has a wheelbase, a track,a height, a speed, a distance between the ego vehicle and the obstacle,and a turning radius. The agent attribute may also be an object thatmoves, such as a pedestrian, a large animal, and a cardboard box blowingin the wind. In some embodiments, the agent attribute may berepresentative of a moving obstacle proximate to the autonomous vehicle.

The attribute data store 810 may include scene attributes 930. Forexample, the scene attributes 930 include road conditions and weatherconditions. Road conditions may include an elevation, a hill steepness,a construction zone, a crosswalk, a stoplight, an HOV lane, a median, atraffic speed, a traffic volume, a number of vehicular and cyclisttraffic lanes, a lane width, lane traffic directions, lane marker types,or a combination thereof. Weather conditions may include rainyconditions, snowy conditions, fog, and thunderstorms. In someembodiments, a scene attribute may be representative of an environmentalobstacle proximate to the autonomous vehicle. Other examples of sceneattributes include a parked vehicle, an object in the roadway, anupcoming intersection, traffic conditions, roadway conditions,construction conditions, intersection conditions, pedestrians, anemergency siren, and/or the like.

The attribute data store 810 may include custom metric attributes 940.Custom metric attributes 940 may include any feature or characteristicof the behavior of the ego vehicle. For example, the ego vehicle behaveswith a brake tap, a gradual coasting stop, cruise control, and the like.Other custom metric attributes 940 may include headlights on, leftturns, right turns, a malfunctioning sensor, a hacked software, pixelnoise at the camera, a false tracked object. These behaviors of the egovehicle may be added to further determine how the machine learning model570 responds.

The attribute data store 810 may be configured to pass the predictionerror and the set of attributes to a query generator 515. In someembodiments, the attributes in the attribute data store 810 may beselected to obtain specific error-prone scenarios. The error-pronescenario may include incorrectly predicted movements of other objects inthe environment. The specific error-prone scenarios may be desirable toidentify to further train the machine learning model 570. For example, adesired error-prone scenario includes an ego vehicle that is drivingproximate to a large truck as the ego vehicle and the large truckapproach a crosswalk with a pedestrian and a stroller. To capturevehicle scenarios matching or similar to this desired scenario, theattributes selected from the attribute data store 810 may include alarge truck as an agent vehicle, a pedestrian detected in the crosswalk,a stroller detected in the crosswalk, and the ego vehicle is 30 feetaway from the crosswalk. In another example, a desired error-pronescenario includes an ego vehicle predicting the movements of largeanimals on country roads at night time. To obtain this desired scenario,the scenario mining controller 590 may select attributes related tonighttime, single-lane highways, and predicting the movement of objectshaving facial features and four-legs.

Referring now to FIG. 10A, illustrated is a diagram of an implementationof a user interface for generating a search string for querying thescenario database 530. The user interface may be representative of howthe prediction errors and attributes are organized and how the searchstring may be generated. Selecting a prediction error and attribute mayenable the scenario mining controller 590 to search through the scenariodatabase 530 using the generated search string without a human manuallysearching through the scenario database 530.

The attribute/prediction error selection module 1010 may present a setof categories representative prediction errors and attributes having acharacteristic of the category. For example, a set of categoriesincludes ego-level attributes, agent-level attributes, scenario-levelattributes, custom metrics attributes, and ML model attributes. Within acategory, such as the ego-level attributes category, attributes mayshare a common feature or descriptor. For example, the ego-levelattributes category includes an ego vehicle speed, a turning radius, adistance between the ego vehicle and an obstacle, a wheelbase, a track,a height, and a maximum deceleration. The set of categories may expandto reveal a list of prediction errors and attributes that share a commoncharacteristic. For example, a category is selected and a drop-down menuis generated including a list of corresponding prediction errors andattributes sharing a similar characteristic with the category. Aprediction error and an attribute may be selected from theattribute/prediction error selection module 1010 for generating a searchstring configured to identify error-prone scenarios having theprediction error.

The search string generator module 1015 may be configured to receive anattribute from the attribute/prediction error selection module 1010 andgenerate a search string based on the prediction error or attribute. Thesearch string generator module 1015 may be configured to format searchstring code and obtain proper syntax capable of performing the query.For example, the search string generator module 1015 is configured toadd Boolean logic and add qualifiers and variables to a function callwithin the search string. In another example, the search stringgenerator module 1015 is configured to obtain variables representativeof the function call and determine threshold values indicative ofconstraints needed in the desired vehicle scenario. The search stringgenerator module 1015 may be configured to rearrange query code asneeded when attributes are added or removed.

In some embodiments, the search string generator module 1015 may beconfigured to generate SQL code. For example, the search stringgenerator module 1015 generates the following example SQL query “SELECTlog_name, sample_token, timestamp FROM agent_prod.all WHERE length >7AND agent_type=′CAR′ AND is_ahead_of_ego=TRUE ANDeuclidean_distance_to_ego<30.0.” The example search string may begenerated in response to adding a “large truck” attribute to the searchstring generator module 1015 that is less than 30 feet in front of theego vehicle.

Referring now to FIG. 10B, illustrated is a diagram of anotherimplementation of the user interface for generating the search stringfor querying the scenario database 530. The user interface may includean attribute/prediction error selection module 1010 and a search stringgenerator module 1015. The attribute/prediction error selection module1010 may present a set of categories representative of prediction errorsand attributes having the characteristic of the category. The searchstring generator module 1015 may be configured to receive a predictionerror and an attribute from the attribute/prediction error selectionmodule 1010 and generate a search string.

Referring now to FIG. 11 , illustrated is a scenario database 530. Ascenario database 530 is searched to identify error-prone scenario 552based on a prediction error and/or an attribute. The scenario database530 includes a plurality of datasets marked with at least one predictionerror to facilitate scenario mining. The scenario database 530 may beconfigured to receive a prediction error and set of attributes as inputand be configured to return an error-prone scenario 552 as output. Thatis, the scenario database 530 may be configured to mine vehiclescenarios based on the prediction error and the set of attributes. Tofacilitate the prediction error scenario mining, the plurality ofscenarios may include metadata for matching attributes from the searchstring to the error-prone scenario 552. Without the scenario database530, brute force training, and experimentation would be the costly andinefficient alternative for the creation of safe and comprehensiveautonomous vehicle machine learning models.

The scenario database 530 may be constructed based on logged drivingexperiences from the vehicle log data store 535. The scenario database530 may be configured to label datasets from the vehicle log data store535 with metadata representative of the features or behaviors in thedataset. The labeled datasets from the vehicle log data store 535 may bestored in the scenario database 530 as a vehicle scenario. The vehiclescenarios may be organized into framesets that includes a plurality offrames that have a discrete timestamp and metadata attached.

The vehicle scenarios in the scenario database 530 may include datasetswith metadata. The datasets may include data from the autonomous vehiclesensor system that is representative of an environment surrounding theautonomous vehicle. The autonomous vehicle sensor system may include aLIDAR sensor dataset, camera sensor dataset, RADAR sensor dataset,telemetrics sensor dataset, and other ego vehicle sensor datasets. Insome embodiments, the sensor data may be simulated. The metadata labelsattached to the vehicle scenarios may include perception error metadata1132, false-positive metadata 1134, final displacement error metadata1136, ego attribute metadata 1138, agent attribute metadata 1140, thescene attribute metadata 1142, machine learning model error attributemetadata 1144, and custom metric attribute metadata 1146.

The perception error metadata 1132 may be added to a dataset includingdata from the autonomous vehicle sensor system. The perception errormetadata 1132 may include the presence of a miscalculation in theplanned movement by the machine learning model 570 in the metadata dueto limited perception and a value representative of a miscalculation inthe planned movement by the machine learning model 570 due to thelimited perception. For example, the perception error metadata 1132includes metadata indicating that the vehicle has a miscalculated U-turncaused by limited perception and a value in the metadata representativeof the miscalculation of the planned movement by the machine learningmodel 570 relative to the ideal movement. The perception error metadata1132 may be added or removed between different framesets. For example,the perception error metadata 1132 is not attached to the framesets inwhich no U-turn is taking place. In another example, perception errormetadata 1132 is added to framesets in which the vehicle is making aU-turn.

The false-positive metadata 1134 is added to a dataset including datafrom the autonomous vehicle sensor system. The false-positive metadata1134 includes the presence of a miscalculation in the planned movementby the machine learning model 570 relative to the ideal movement in themetadata caused by unnecessary movement or brake tap and a valuerepresentative of a miscalculation in the planned movement by themachine learning model 570 caused by an unnecessary movement or braketap. For example, the false-positive metadata 1134 includes metadataindicating that the vehicle has a brake tap caused by a false perceptionof a stop sign and a value in the metadata representative of themiscalculation of the planned movement by the machine learning model 570relative to the ideal movement of not stopping. The false-positivemetadata 1134 may be added or removed between different framesets. Forexample, the false-positive metadata 1134 is not attached to theframesets in which no false movements in response to a stop sign takeplace. The false-positive metadata 1134 includes the presence of amiscalculation of a predicted movement of an object that cause anunnecessary movement planned by the machine learning model 570. Inanother example, false-positive metadata 1134 is added to framesets inwhich the vehicle is falsely predicting the movement of an approachingbicyclist.

The final displacement error metadata 1136 may be added to a datasetincluding data from the autonomous vehicle sensor system. The finaldisplacement error metadata 1136 may include the presence of amiscalculation in the planned movement by the machine learning model 570relative to the ideal movement in the metadata resulting in theautonomous vehicle moving to a location other than the desired location.The final displacement error metadata 1136 may include a valuerepresentative of a miscalculation in the planned movement by themachine learning model 570 that is the difference between the plannedmovement and the ideal movement. For example, the final displacementerror metadata 1136 includes metadata indicating that the vehicle hasimproperly parallel parked and a value in the metadata representative ofthe distance in the miscalculation of the parallel parking by themachine learning model 570 relative to the ideal parallel parking. Thefinal displacement error metadata 1136 may be added or removed betweendifferent framesets. For example, the final displacement error metadata1136 is not attached to the framesets in which no miscalculatedmovements occurred in response to parallel parking. The finaldisplacement error metadata 1136 may include the presence of amiscalculation in the predicted movement of other objects by the machinelearning model 570 relative to the ideal movement prediction of otherobjects. For example, final displacement error metadata 1136 is added toframesets in which the vehicle is incorrectly predicts the movements ofvehicles behind the autonomous vehicle while parallel parking theautonomous vehicle.

The ego attribute metadata 1138 may be added to a dataset including datafrom the autonomous vehicle sensor system. The ego attribute metadata1138 may include the presence of a sensor in the metadata and a valuerepresentative of data gathered by the sensor related to the egovehicle. For example, the ego attribute metadata 1138 includes metadataindicating that the vehicle has a speed sensor and a value in themetadata representative of the speed of a vehicle within a particularframe or frameset. The ego attribute metadata 1138 may be added orremoved between different framesets. For example, the ego attributemetadata 1138 for the speed sensor is not attached to the framesets inwhich the vehicle is idle. In another example, braking sensor metadataand a metadata value indicative of a rate at which the vehicle isdecelerating may be added to framesets in which the vehicle is braking.

The agent attribute metadata 1140 may be added to a dataset includingdata from the autonomous vehicle sensor system. The agent attributemetadata 1140 may include the presence of a sensor in the metadata and avalue representative of data gathered by the sensor related to movingobjects around the vehicle. For example, the agent attribute metadata1140 includes metadata indicating that a tractor is approaching in theopposite direction over an overpass within a particular frameset. Theagent attribute metadata 1140 may be added or removed between differentframesets. For example, the agent attribute metadata 1140 recognizingthe tractor is not attached to the framesets once the tractor has passedthe ego vehicle. In another example, the “large animal” metadata and ametadata indicative of a rate at which the detected large animal ismoving is added to the framesets in which a large animal is detected.

The scene attribute metadata 1142 may be added to a dataset includingdata from the autonomous vehicle sensor system. The scene attributemetadata 1142 may include the presence of a feature in the environmentsurrounding the ego vehicle and a value representative of the intensityof the feature in the environment surrounding the ego vehicle. Forexample, the scene attribute metadata 1142 includes metadata indicatingthat the ego vehicle is approaching a steep hill having a guardrail anda dropoff on one side and that the hill has a specific gradient (i.e., avalue representative of the intensity of the feature in theenvironment). The scene attribute metadata 1142 may be added or removedbetween different framesets. For example, the scene attribute metadata1142 recognizing the steep hill is not attached to the framesets oncethe ego vehicle has passed the steep hill. In another example, the“snow” metadata and a metadata indicative of the rate of snowfall isadded to the framesets when it is snowing.

The machine learning model error attribute metadata 1144 may be added toa dataset in which there is a difference between the planned movementand the actual movement of the vehicle. The machine learning model errorattribute metadata 1144 may include the presence of a prediction errorfrom the machine learning model 570 and a value representative of theintensity of the error of the machine learning model 570. For example,the machine learning model error attribute metadata 1144 includesmetadata indicating that the machine learning model 570 originallymiscalculated the timing needed to make a left-hand turn based on apredicted movement of the nearby vehicle and the difference between thecalculated timing and the corrected timing based on the actual movementof the nearby vehicle. The machine learning model error attributemetadata 1144 may be added or removed between different framesets. Forexample, the machine learning model error attribute metadata 1144includes attached to framesets including a U-turn in which no differencebetween the planned U-turn movement and the actual U-turn movement ofvehicle. In another example, the “overtake” metadata and a metadataindicative of the error in overtaking a slower vehicle on the highway isadded to the framesets in a vehicle scenario where the ego vehicleovertakes a vehicle on the highway. In some embodiments, the machinelearning model 570 error may be simulated.

The custom metric attribute metadata 1146 may be added to a datasetincluding data having a particular function, feature, or characteristicof the behavior of the ego vehicle. For example, the function orbehavior of the ego vehicle includes a brake tap, a gradual coastingstop, and a cruise control. The custom metric attribute metadata 1146may include the presence of a behavior or function of the ego vehicleand a value representative of the intensity or duration of the featureof the ego vehicle. For example, the custom metric attribute metadata1146 includes metadata indicating that the ego vehicle is in cruisecontrol mode and a duration of the cruise control mode. The custommetric attribute metadata 1146 may be added or removed between differentframesets. For example, the custom metric attribute metadata 1146indicating a coasted stop is not attached to the framesets once the egovehicle has coasted to a complete stop. In another example, the “braketap” metadata and a metadata indicative of the force the user appliesthe brake during a planned movement of the autonomous vehicle is addedto the framesets during a time period before and after the brake tap. Insome embodiments, the behaviors or features of the custom metricattribute metadata 1146 may be simulated.

Referring now to FIG. 12 , illustrated is a diagram of an example of auser interface for the matching simulations data store 550. Theerror-prone scenario 552 from the scenario database 530 may thenobtained for inputting into the machine learning model 570 for trainingthe machine learning model 570 on the error-prone scenario 552. Thistechnique may identify error-prone scenarios to determine how thevehicle's systems would handle such scenarios in the real world. Theerror-prone scenario 552 may be indicative of a scenario in which theplanned movements of the machine learning model 570 are uncertain.Without identifying error-prone scenarios and their associated metrics,it may be unclear the extent of the effect that any one of theseuncommon scenarios would have on the autonomous vehicle's ability tocontinue navigation.

The scenarios obtained from the scenario database 530 may be stored inthe matching simulations data store 550. The matching simulations datastore 550 may include the obtained scenarios having the queriedattribute. For example, the matching simulations data store 550 includesthe obtained vehicle scenarios based on the queried attribute “largeanimal” or obtained vehicle scenarios based on the combination ofqueried attributes of “stroller,” “crosswalk,” “intersection,” and “30feet from ego vehicle.” The different sets of returned vehicle scenariosmay be organized into categories that are displayed in a drop-down menuat the user interface 1200 for the matching simulations data store 550.

In some embodiments, the vehicle scenarios obtained from the scenariodatabase 530 may be a dataset a plurality of frames. Each frame of theplurality of frames has a time stamp at a discrete time interval andattribute metadata. The attribute metadata may be based on the datareceived from the autonomous vehicle sensor system corresponding to thetime stamp. In some embodiments, the scenario mining controller 590 mayobtain the plurality of frames having the attribute metadata associatedwith the at least one attribute of the set of attributes. In someembodiments, each frame of the vehicle scenario may be marked with theat least one attribute of the set of attributes.

Referring now to FIG. 13 , illustrated is a flowchart of a process 1100for prediction error scenario mining for machine learning models. Insome embodiments, one or more of the steps described with respect toprocess 1100 are performed (e.g., completely, partially, and/or thelike) by the scenario mining controller 590. Additionally, oralternatively, in some embodiments, one or more steps described withrespect to process 1100 are performed (e.g., completely, partially,and/or the like) by another device or group of devices separate from orincluding the scenario mining controller 590.

At 1302, a prediction error is determined that is indicative of adifference between a planned decision of an autonomous vehicle and anideal decision of the autonomous vehicle. The prediction error isassociated with an error-prone scenario 552 for which the machinelearning model 570 of an autonomous vehicle is to make plannedmovements. For example, an error-prone scenario 552 having a predictionerror includes an ego vehicle that is stopped proximate to a large truckat an intersection including a crosswalk with a pedestrian as the egovehicle plans to make an unprotected left-hand turn based on thepredicted movement of nearby vehicles. To capture error-prone scenariossimilar to this desired error-prone scenario, the prediction error maybe selected from the prediction error data store 510 and may includeerror modes or error types related to an agent vehicle that is a largetruck, a pedestrian detected in a crosswalk at an intersection, and aplanned decision to make an unprotected left-hand turn based on thepredicted movement of nearby vehicles.

At 1304, a scenario database 530 is searched for the error-pronescenario 552 based on the prediction error. The scenario database 530includes a plurality of datasets representative of data received from anautonomous vehicle sensor system. For example, a scenario database 530searches through metadata associated with datasets from an autonomousvehicle sensor system to identify an error-prone scenario 552 having aprediction error with metadata that includes an ego vehicle that isstopped proximate to a large truck at an intersection including acrosswalk with a pedestrian as the ego vehicle plans to make anunprotected left-hand turn based on the predicted movement of nearbyvehicles.

At 1306, the error-prone scenario 552 is obtained from the scenariodatabase 530 for inputting into the machine learning model 570 fortraining the machine learning model 570. The error-prone scenario 552includes a dataset from the plurality of datasets in the scenariodatabase. The machine learning model 570 may be configured to make theplanned movements for the autonomous vehicle. For example, the obtainederror-prone scenario 552 of an ego vehicle stopped proximate to a largetruck at an intersection including a crosswalk with a pedestrian as theego vehicle plans to make an unprotected left-hand turn based on thepredicted movement of nearby vehicles is inputted to an online machinelearning model for further training of the offline machine learningmodel.

In the foregoing description, aspects and embodiments of the presentdisclosure have been described with reference to numerous specificdetails that can vary from implementation to implementation.Accordingly, the description and drawings are to be regarded in anillustrative rather than a restrictive sense. The sole and exclusiveindicator of the scope of the invention, and what is intended by theapplicants to be the scope of the invention, is the literal andequivalent scope of the set of claims that issue from this application,in the specific form in which such claims issue, including anysubsequent correction. Any definitions expressly set forth herein forterms contained in such claims shall govern the meaning of such terms asused in the claims. In addition, when we use the term “furthercomprising,” in the foregoing description or following claims, whatfollows this phrase can be an additional step or entity, or asub-step/sub-entity of a previously-recited step or entity.

What is claimed is:
 1. A system, comprising: at least one dataprocessor; and at least one memory storing instructions, which whenexecuted by at least one data processor, result in operationscomprising: identifying a first scenario encountered by a vehicle, thefirst scenario being identified based at least on a firsterror in anoutput of an online machine learning model trained to plan a movement ofthe vehicle while the vehicle encounters the first scenario satisfying afirst threshold; generating a first label for the first scenario byapplying an offline machine learning model also trained to plan themovement of the vehicle; generating training data including the firstscenario and the first label associated with the first scenario; andupdating, based at least on the training data, the online machinelearning model to plan the movement of the vehicle.
 2. The system ofclaim 1, wherein the first error in the output of the online machinelearning model includes a prediction error comprising a displacementbetween a trajectory output by the online machine learning model and aground truth trajectory associated with the first scenario.
 3. Thesystem of claim 1, wherein the first error in the output of the onlinemachine learning model includes a perception error comprising adifference between a classification of an object present in the firstscenario determined by the online machine learning model and a groundtruth classification of the object.
 4. The system of claim 3, whereinthe operations further comprise: determining that the prediction errorassociated with determining a trajectory for the first scenariosatisfies the first threshold based at least on the perception errorassociated with identifying one or more objects present in the firstscenario satisfying a second threshold; and identifying the firstscenario based at least on the prediction error associated with thefirst scenario exceeding the first threshold.
 5. The system of claim 1,wherein the online machine learning model and the offline machinelearning model are trained to classify one or more objects present in anenvironment in which the vehicle is operating.
 6. The system of claim 1,wherein the online machine learning model and the offline machinelearning model are trained to determine a trajectory for controlling themovement of the vehicle.
 7. The system of claim 1, wherein theoperations further comprise: identifying a second scenario encounteredby the vehicle, the second scenario being identified based at least on asecond error in the output of the online machine learning model whilethe vehicle encounters the second scenario satisfying the firstthreshold; applying the offline machine learning model to generate asecond label for the second scenario; applying the updated onlinemachine learning model to the second scenario; and sending the updatedonline machine learning model to one or more vehicles in response to adifference between the output of the updated machine learning modeloperating on the second scenario and the second label associated withthe second scenario satisfying a second threshold.
 8. The system ofclaim 1, wherein the first scenario includes a combination of attributesassociated with the at least one of the vehicle, an environment in whichthe vehicle is operating, and an object in the environment.
 9. Thesystem of claim 8, wherein the first scenario is further identifiedbased at least on a difficulty level associated with at least oneattribute in the combination of attributes.
 10. The system of claim 8,wherein the first scenario is further identified based at least on afrequency at which the online machine learning model encounters at leastone attribute in the combination of attributes.
 11. The system of claim1, wherein the offline machine learning model is deployed at a remoteserver to generate the first label for the first scenario, and whereinthe updating of the online machine learning model is performed by theremote server before the updated online machine learning model isdeployed at one or more vehicles.
 12. The system of claim 1, wherein theoperations further comprise: querying a scenario database to select thefirst scenario, the scenario database storing a plurality of scenarios,the scenario database further storing, for each scenario of theplurality of scenarios, a respective error in the output of the onlinemachine learning model when planning a corresponding movement of thevehicle while the vehicle encounters each scenario.
 13. The system ofclaim 12, wherein each scenario in the scenario database furtherincludes one or more frames, wherein each frame of the one or moreframes is associated with at least one attribute, and wherein each frameof the one or more frames is further associated with a timestamp. 14.The system of claim 12, wherein the operations further comprise:extracting, from data logged by one or more vehicles, a combination ofone or more vehicle level attributes, environment level attributes, andobject level attributes associated with each scenario included in thescenario database.
 15. The system of claim 12, wherein the data loggedby the one or more vehicles include at least one of LIDAR sensor data,camera sensor data, RADAR sensor data, and telemetrics sensor data. 16.A method, comprising: identifying, using at least one processor, a firstscenario encountered by a vehicle, the first scenario being identifiedbased at least on a firsterror in an output of an online machinelearning model trained to plan a movement of the vehicle while thevehicle encounters the first scenario satisfying a first threshold;generating, using the at least one processor, a first label for thefirst scenario by applying an offline machine learning model alsotrained to plan the movement of the vehicle; generating, using the atleast the one processor, training data including the first scenario andthe first label associated with the first scenario; and updating, usingthe at least one processor and based at least on the training data, theonline machine learning model to plan the movement of the vehicle. 17.The method of claim 16, wherein the first error in the output of theonline machine learning model includes a prediction error comprising adisplacement between a trajectory output by the online machine learningmodel and a ground truth trajectory associated with the first scenario.18. The method of claim 16, wherein the first error in the output of theonline machine learning model includes a perception error comprising adifference between a classification of an object present in the firstscenario determined by the online machine learning model and a groundtruth classification of the object.
 19. The method of claim 18, furthercomprising: determining, using the at least one processor, that theprediction error associated with determining a trajectory for the firstscenario satisfies the first threshold based at least on the perceptionerror associated with identifying one or more objects present in thefirst scenario satisfying a second threshold; and identifying, using theat least one processor, the first scenario based at least on theprediction error associated with the first scenario exceeding the firstthreshold.
 20. The method of claim 16, wherein the online machinelearning model and the offline machine learning model are trained toclassify one or more objects present in an environment in which thevehicle is operating.
 21. The method of claim 16, wherein the onlinemachine learning model and the offline machine learning model aretrained to determine a trajectory for controlling the movement of thevehicle.
 22. The method of claim 16, further comprising: identifying,using the at least one data processor, a second scenario encountered bythe vehicle, the second scenario being identified based at least on asecond error in the output of the online machine learning model whilethe vehicle encounters the second scenario satisfying the firstthreshold; applying, using the at least one processor, the offlinemachine learning model to generate a second label for the secondscenario; applying, using the at least one processor, the updated onlinemachine learning model to the second scenario; and sending, using the atleast one processor, the updated online machine learning model to one ormore vehicles in response to a difference between the output of theupdated machine learning model operating on the second scenario and thesecond label associated with the second scenario satisfying a secondthreshold.
 23. The method of claim 16, wherein the first scenarioincludes a combination of attributes associated with the at least one ofthe vehicle, an environment in which the vehicle is operating, and anobject in the environment.
 24. The method of claim 23, wherein the firstscenario is further identified based at least on a difficulty levelassociated with at least one attribute in the combination of attributes.25. The method of claim 23, wherein the first scenario is furtheridentified based at least on a frequency at which the online machinelearning model encounters at least one attribute in the combination ofattributes.
 26. The method of claim 16, wherein the offline machinelearning model is deployed at a remote server to generate the firstlabel for the first scenario, and wherein the updating of the onlinemachine learning model is performed by the remote server before theupdated online machine learning model is deployed at one or morevehicles.
 27. The method of claim 16, further comprising: querying,using the at least one processor, a scenario database to select thefirst scenario, the scenario database storing a plurality of scenarios,the scenario database further storing, for each scenario of theplurality of scenarios, a respective error in the output of the onlinemachine learning model when planning a corresponding movement of thevehicle while the vehicle encounters each scenario.
 28. The method ofclaim 27, wherein each scenario in the scenario database furtherincludes one or more frames, wherein each frame of the one or moreframes is associated with at least one attribute, and wherein each frameof the one or more frames is further associated with a timestamp. 29.The method of claim 27, further comprising: extracting, from data loggedby one or more vehicles, a combination of one or more vehicle levelattributes, environment level attributes, and object level attributesassociated with each scenario included in the scenario database.
 30. Anon-transitory computer readable medium storing instructions, which whenexecuted by at least one data processor, result in operationscomprising: identifying a first scenario encountered by a vehicle, thefirst scenario being identified based at least on a firsterror in anoutput of an online machine learning model trained to plan a movement ofthe vehicle while the vehicle encounters the first scenario satisfying afirst threshold; generating a first label for the first scenario byapplying an offline machine learning model also trained to plan themovement of the vehicle; generating training data including the firstscenario and the first label associated with the first scenario; andupdating, based at least on the training data, the online machinelearning model to plan the movement of the vehicle.